Sonya M. Hanson

The paper introduces the CAHRA Challenge, a community-wide assessment of cryo-EM heterogeneous reconstruction algorithms using three benchmark datasets.

cryoWEight integrates cryo-EM data with weighted ensemble simulations to generate conformational ensembles of biomolecules.

PYSTARC estimates bimolecular association rate constants using GPU-accelerated Brownian dynamics, achieving accuracy within one order of magnitude of experimental values.

This study uses deep learning and physics-based modeling to engineer a CD27 variant that improves the efficacy of CD70-targeting CAR T-cells across multiple cancer models.

seekrflow automates drug-target kinetic and thermodynamic predictions using a multiscale milestoning simulation pipeline, achieving accurate results with reduced manual intervention and computational cost.

This study uses molecular dynamics simulations to show how amphipathic helix domains cooperate in binding to lipid membranes, stabilizing septin structures.

This study uses molecular dynamics simulations to investigate how vitrification affects the conformational ensemble of Trp-cage miniprotein and develops a thermodynamic inference framework to recover equilibrium populations from vitrified samples.

The paper introduces cryoJAX, a cryo-EM image simulation library built on JAX for developing data analysis techniques in cryo-EM.

The paper compares methods for analyzing heterogeneity in TRPV1 cryo-EM data and finds that different methods detect different types of heterogeneity.

This study uses multiscale simulations to investigate MUT-16 phase separation and its role in recruiting MUT-8, confirming key interactions through in vitro experiments.

The paper presents a cryo-EM conformational heterogeneity challenge to evaluate and improve methods for analyzing molecular motions in heterogeneous samples.

The paper develops a coarse-grained microtubule model using Martini 3 and elastic networks to accurately simulate mechanical properties at a larger scale than atomistic simulations.

PLANET-MD predicts dynamic protein properties from sequence or structure with high accuracy and speed, enabling comprehensive proteome-scale analysis of protein dynamics.

ExEnDiff generates protein conformations using experimental data as a prior, improving diversity and physical realism over existing methods.

This study investigates how charge distribution and helical content of amphipathic helix domains affect septin binding to lipid membranes using simulations and experiments.

This study investigates RNA polymerase II condensates in C. elegans using simulations and experiments, showing they exhibit LCST behavior and form distinct phases during transcription.